boilerplate-demographic-data-analyzer

python 题

import pandas as pd


def calculate_demographic_data(print_data=True):
    # Read data from file
    df = pd.read_csv('adult.data.csv')

    # How many of each race are represented in this dataset? This should be a Pandas series with race names as the index labels.
    race_count = df['race'].value_counts()

    # What is the average age of men?
    average_age_men = round(df[df['sex'] == 'Male']['age'].mean(), 1)

    # What is the percentage of people who have a Bachelor's degree?
    percentage_bachelors = round(df['education'].value_counts()['Bachelors']/len(df)*100, 1)

    # What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?

    # What percentage of people without advanced education make more than 50K?
    
    # with and without `Bachelors`, `Masters`, or `Doctorate`
    higher_education = df[df['education'].isin(['Bachelors', 'Masters','Doctorate'])]
    lower_education = df[~df['education'].isin(['Bachelors', 'Masters','Doctorate'])]

    # percentage with salary >50K

    higher_education_rich = round(len(higher_education[higher_education['salary']=='>50K'])/len(higher_education)*100, 1)                                     
    lower_education_rich = round(len(lower_education[lower_education['salary']=='>50K'])/len(lower_education)*100, 1)    
                        
    # What is the minimum number of hours a person works per week (hours-per-week feature)?
    min_work_hours = df['hours-per-week'].min()

    # What percentage of the people who work the minimum number of hours per week have a salary of >50K?
    num_min_workers = len(df[(df['hours-per-week']==min_work_hours)])

    rich_percentage = round(len(df[(df['hours-per-week']==min_work_hours) & (df['salary']=='>50K')])/num_min_workers*100, 1)

    # What country has the highest percentage of people that earn >50K?
    highest_earning_country = (df.loc[df["salary"] == ">50K", "native-country"].value_counts() / df["native-country"].value_counts()).fillna(0).sort_values(ascending=False).index[0]
    
    highest_earning_country_percentage = round(len(df[(df["native-country"] == highest_earning_country) & (
        df["salary"] == ">50K")]) / len(df[df["native-country"] == highest_earning_country]) * 100, 1)
    # Identify the most popular occupation for those who earn >50K in India.
    top_IN_occupation = df[(df['salary']=='>50K') & (df['native-country']=='India')]['occupation'].value_counts().index[0]

    # DO NOT MODIFY BELOW THIS LINE

    if print_data:
        print("Number of each race:\n", race_count) 
        print("Average age of men:", average_age_men)
        print(f"Percentage with Bachelors degrees: {
      
      percentage_bachelors}%")
        print(f"Percentage with higher education that earn >50K: {
      
      higher_education_rich}%")
        print(f"Percentage without higher education that earn >50K: {
      
      lower_education_rich}%")
        print(f"Min work time: {
      
      min_work_hours} hours/week")
        print(f"Percentage of rich among those who work fewest hours: {
      
      rich_percentage}%")
        print("Country with highest percentage of rich:", highest_earning_country)
        print(f"Highest percentage of rich people in country: {
      
      highest_earning_country_percentage}%")
        print("Top occupations in India:", top_IN_occupation)

    return {
    
    
        'race_count': race_count,
        'average_age_men': average_age_men,
        'percentage_bachelors': percentage_bachelors,
        'higher_education_rich': higher_education_rich,
        'lower_education_rich': lower_education_rich,
        'min_work_hours': min_work_hours,
        'rich_percentage': rich_percentage,
        'highest_earning_country': highest_earning_country,
        'highest_earning_country_percentage':
        highest_earning_country_percentage,
        'top_IN_occupation': top_IN_occupation
    }